A　growing　number　of　organizations　deploy　multiple　heterogeneous　applications　in　infrastructures　of　distributed　green　data　centers　(DGDCs)　to　flexibly　provide　services　to　users　around　the　world　in　a　low-cost　and　high-quality　way.　The　skyrocketing　growth　in　types　and　number　of　heterogeneous　applications　dramatically　increases　the　amount　of　energy　consumed　by　DGDCs.　The　spatial　and　temporal　variations　in　prices　of　power　grid　and　availability　of　renewable　energy　make　it　highly　challenging　to　minimize　the　energy　cost　of　DGDC　providers　by　intelligently　scheduling　arriving　tasks　of　heterogeneous　applications　among　GDCs　while　meeting　their　expected　delay　bound　constraints.　Unlike　existing　studies,　this　paper　proposes　a　spatiotemporal　task　scheduling　(STTS)　algorithm　to　minimize　energy　cost　by　cost-effectively　scheduling　all　arriving　tasks　to　meet　their　delay　bound　constraints.　STTS　well　investigates　spatial　and　temporal　variations　in　DGDCs.　In　each　time　slot,　the　energy　cost　minimization　problem　is　formulated　as　a　nonlinear　constrained　optimization　one　and　addressed　with　the　proposed　genetic　simulated-annealing-based　particle　swarm　optimization.　Trace-driven　experiments　show　that　STTS　achieves　larger　throughput　and　lower　energy　cost　than　several　typical　task　scheduling　approaches　while　strictly　meeting　all　tasks＇　delay　bound　constraints.　Note　to　Practitioners　This　paper　investigates　the　energy　cost　minimization　problem　for　a　DGDC　provider　while　meeting　delay　bound　constraints　for　all　arriving　tasks.　Previous　scheduling　methods　do　not　jointly　consider　spatial　and　temporal　variations　in　prices　of　power　grid　and　availability　of　renewable　energy　in　DGDCs.　Therefore,　they　fail　to　adopt　such　variations　to　minimize　the　energy　cost　of　a　DGDC　provider.　In　this　paper,　a　new　method　that　avoids　disadvantages　of　previous　methods　is　proposed.　It　is　realized　by　adopting　a　hybrid　metaheuristic　algorithm　named　GSP　to　solve　a　nonlinear　constrained　optimization　problem.　Experimental　results　demonstrate　that　compared　with　several　typical　methods,　it　reduces　energy　cost　and　increases　throughput.　It　can　be　readily　integrated　into　realistic　industrial　DGDCs.　The　future　work　requires　engineers　to　consider　the　effect　of　indeterminacy　and　uncertainty　of　green　energy　on　scheduling　methods.